Leaf Area Index Variation for Crop, Pasture, and Tree in Response to Climatic Variation in the Goulburn–Broken Catchment, Australia

Abstract Previous studies have reported relationships between mean annual climatic variables and mean annual leaf area index (LAI), but the seasonal and spatial variability of this relationship for different vegetation cover types in different climate zones have rarely been explored in Australia. The authors developed simple models using remotely sensed LAI data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and gridded climatic data from the Australian Water Availability Project. They were able to relate seasonal and annual LAI of three different land cover types (tree, pasture, and crop) with climatic variables for the period 2000–09 in the Goulburn–Broken catchment, Australia. Strong relationships were obtained between annual LAI of crop, pasture, and tree with annual precipitation (R2 = 0.70, 0.65, and 0.82, respectively). Monthly LAI of each land cover type also showed a strong relationship (R2 = 0.92, 0.95, and 0.95) with the difference between precipitation P and reference crop evapotranspiration (PET; P − PET) for crop, pasture, and tree. Independent model calibration and validation showed good agreement with remotely sensed MODIS LAI. The results from the application of the developed model on the future impact of climate change suggest that under all climate scenarios crop, pasture, and tree showed consistent decreases in mean annual LAI. For the future climate change scenarios considered, crop showed a decline of 7%–38%, pasture showed a decline of 5%–24%, and tree showed a decline of 2%–11% from the historical mean annual. These results can be used to assess the impacts of future climatic and land cover changes on water resources by coupling them with hydrological models.

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Assessment of Climate Change Impact on Crop yield and Evaluation of Coping Strategy using Crop Growth Simulation Model

Journal of the Institute of Engineering ◽

10.3126/jie.v15i1.27719 ◽

2020 ◽

Vol 15 (1) ◽

pp. 106-122

Author(s):

J. Alam ◽

R. K. Panda

Keyword(s):

Climate Change ◽

Grain Yield ◽

Simulation Model ◽

Leaf Area Index ◽

Leaf Area ◽

Co2 Concentration ◽

Maize Yield ◽

Crop Growth ◽

Climate Change Scenarios ◽

Area Index

Any change in climate will have implications for climate-sensitive systems such as agriculture, forestry and some other natural resources. Changes in solar radiation, temperature and precipitation will produce changes in crop yields and hence economics of agriculture. It is possible to understand the phenomenon of climate change on crop production and to develop adaptation strategies for sustainability in food production, using a suitable crop simulation model. CERES-Maize model of DSSAT v4.0 was used to simulate the maize yield of the region under climate change scenarios using the historical weather data at Kharagpur (1977-2007), Damdam (1974-2003) and Purulia (1986-2000), West Bengal, India. The model was calibrated using the crop experimental data, climate data and soil data for two years (1996-1997) and was validated by using the data of the year 1998 at Kharagpur. The change in values of weather parameters due to climate change and its effects on the maize crop growth and yield was studied. It was observed that increase in mean temperature and leaf area index have negative impacts on maize yield. When the maximum leaf area index increased, the grain yield was found to be decreased. Increase in CO2 concentration with each degree incremental temperature decreased the grain yield but increase in CO2 concentration with fixed temperature increased the maize yield. Adjustments were made in the date of sowing to investigate suitable option for adaptation under the future climate change scenarios. Highest yield was obtained when the sowing date was advanced by a week at Kharagpur and Damdam whereas for Purulia, the experimental date of sowing was found to be beneficial.

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Monitoring wheat phenology and irrigation in Central Morocco: On the use of relationships between evapotranspiration, crops coefficients, leaf area index and remotely-sensed vegetation indices

Agricultural Water Management ◽

10.1016/j.agwat.2005.02.013 ◽

2006 ◽

Vol 79 (1) ◽

pp. 1-27 ◽

Cited By ~ 246

Author(s):

B. Duchemin ◽

R. Hadria ◽

S. Erraki ◽

G. Boulet ◽

P. Maisongrande ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Vegetation Indices ◽

Remotely Sensed ◽

Area Index

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Climate, ticks and disease

10.1079/9781789249637.0000 ◽

2021 ◽

Keyword(s):

Climate Change ◽

Spatial Distribution ◽

Disease Ecology ◽

Future Climate Change ◽

Climate Change Scenarios ◽

Expert Opinions ◽

The World ◽

Host Pathogen ◽

The Future ◽

Impacts Of Climate Change

Abstract This book is a collection of 77 expert opinions arranged in three sections. Section 1 on "Climate" sets the scene, including predictions of future climate change, how climate change affects ecosystems, and how to model projections of the spatial distribution of ticks and tick-borne infections under different climate change scenarios. Section 2 on "Ticks" focuses on ticks (although tick-borne pathogens creep in) and whether or not changes in climate affect the tick biosphere, from physiology to ecology. Section 3 on "Disease" focuses on the tick-host-pathogen biosphere, ranging from the triangle of tick-host-pathogen molecular interactions to disease ecology in various regions and ecosystems of the world. Each of these three sections ends with a synopsis that aims to give a brief overview of all the expert opinions within the section. The book concludes with Section 4 (Final Synopsis and Future Predictions). This synopsis attempts to summarize evidence provided by the experts of tangible impacts of climate change on ticks and tick-borne infections. In constructing their expert opinions, contributors give their views on what the future might hold. The final synopsis provides a snapshot of their expert thoughts on the future.

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Integrating remote sensing and climate data with process-based models to map forest productivity within west-central Alberta's boreal forest: Ecoleap-West

The Forestry Chronicle ◽

10.5558/tfc82159-2 ◽

2006 ◽

Vol 82 (2) ◽

pp. 159-176 ◽

Cited By ~ 16

Author(s):

R J Hall ◽

F. Raulier ◽

D T Price ◽

E. Arsenault ◽

P Y Bernier ◽

...

Keyword(s):

Climate Change ◽

Remote Sensing ◽

Leaf Area Index ◽

Leaf Area ◽

Management Practices ◽

Forest Productivity ◽

Process Models ◽

Growth And Yield ◽

Area Index ◽

Study Region

Forest yield forecasting typically employs statistically derived growth and yield (G&Y) functions that will yield biased growth estimates if changes in climate seriously influence future site conditions. Significant climate warming anticipated for the Prairie Provinces may result in increased moisture deficits, reductions in average site productivity and changes to natural species composition. Process-based stand growth models that respond realistically to simulated changes in climate can be used to assess the potential impacts of climate change on forest productivity, and hence can provide information for adapting forest management practices. We present an application of such a model, StandLEAP, to estimate stand-level net primary productivity (NPP) within a 2700 km2 study region in western Alberta. StandLEAP requires satellite remote-sensing derived estimates of canopy light absorption or leaf area index, in addition to spatial data on climate, topography and soil physical characteristics. The model was applied to some 80 000 stand-level inventory polygons across the study region. The resulting estimates of NPP correlate well with timber productivity values based on stand-level site index (height in metres at 50 years). This agreement demonstrates the potential to make site-based G&Y estimates using process models and to further investigate possible effects of climate change on future timber supply. Key words: forest productivity, NPP, climate change, process-based model, StandLEAP, leaf area index, above-ground biomass

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Watershed Modeling with Remotely Sensed Big Data: MODIS Leaf Area Index Improves Hydrology and Water Quality Predictions

Remote Sensing ◽

10.3390/rs12132148 ◽

2020 ◽

Vol 12 (13) ◽

pp. 2148 ◽

Cited By ~ 1

Author(s):

Adnan Rajib ◽

I Luk Kim ◽

Heather E. Golden ◽

Charles R. Lane ◽

Sujay V. Kumar ◽

...

Keyword(s):

Water Quality ◽

Soil Moisture ◽

Leaf Area Index ◽

Leaf Area ◽

Vegetation Dynamics ◽

Watershed Modeling ◽

Remotely Sensed ◽

Area Index ◽

Daily Streamflow ◽

Modis Lai

Traditional watershed modeling often overlooks the role of vegetation dynamics. There is also little quantitative evidence to suggest that increased physical realism of vegetation dynamics in process-based models improves hydrology and water quality predictions simultaneously. In this study, we applied a modified Soil and Water Assessment Tool (SWAT) to quantify the extent of improvements that the assimilation of remotely sensed Leaf Area Index (LAI) would convey to streamflow, soil moisture, and nitrate load simulations across a 16,860 km2 agricultural watershed in the midwestern United States. We modified the SWAT source code to automatically override the model’s built-in semiempirical LAI with spatially distributed and temporally continuous estimates from Moderate Resolution Imaging Spectroradiometer (MODIS). Compared to a “basic” traditional model with limited spatial information, our LAI assimilation model (i) significantly improved daily streamflow simulations during medium-to-low flow conditions, (ii) provided realistic spatial distributions of growing season soil moisture, and (iii) substantially reproduced the long-term observed variability of daily nitrate loads. Further analysis revealed that the overestimation or underestimation of LAI imparted a proportional cascading effect on how the model partitions hydrologic fluxes and nutrient pools. As such, assimilation of MODIS LAI data corrected the model’s LAI overestimation tendency, which led to a proportionally increased rootzone soil moisture and decreased plant nitrogen uptake. With these new findings, our study fills the existing knowledge gap regarding vegetation dynamics in watershed modeling and confirms that assimilation of MODIS LAI data in watershed models can effectively improve both hydrology and water quality predictions.

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Evaluation of topographic effects on multiscale leaf area index estimation using remotely sensed observations from multiple sensors

ISPRS Journal of Photogrammetry and Remote Sensing ◽

10.1016/j.isprsjprs.2019.06.008 ◽

2019 ◽

Vol 154 ◽

pp. 176-188 ◽

Cited By ~ 3

Author(s):

Huaan Jin ◽

Ainong Li ◽

Weixing Xu ◽

Zhiqiang Xiao ◽

Jingyi Jiang ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Remotely Sensed ◽

Topographic Effects ◽

Area Index ◽

Multiple Sensors ◽

Index Estimation

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Seasonal Net Carbon Exchange in Rotation Crops in the Temperate Climate of Central Lithuania

Sustainability ◽

10.3390/su11071966 ◽

2019 ◽

Vol 11 (7) ◽

pp. 1966 ◽

Cited By ~ 2

Author(s):

Ligita Baležentienė ◽

Ovidijus Mikša ◽

Tomas Baležentis ◽

Dalia Streimikiene

Keyword(s):

Climate Change ◽

Exchange Rate ◽

Soil Respiration ◽

Leaf Area Index ◽

Leaf Area ◽

Co2 Emissions ◽

Vegetation Period ◽

Temperate Climate ◽

Carbon Exchange ◽

Area Index

Intelligent agricultural solutions require data on the environmental impacts of agriculture. In order for operationalize decision-making for sustainable agriculture, one needs to establish the corresponding datasets and protocols. Increasing anthropogenic CO2 emissions into the atmosphere force the choice of growing crops aimed at mitigating climate change. For this reason, investigations of seasonal carbon exchange were carried out in 2013–2016 at the Training Farm of the Vytautas Magnus University (former Aleksandras Stulginskis University), Lithuania. This paper compares the carbon exchange rate for different crops, viz., maize, ley, winter wheat, spring rapeseed and barley under conventional farming. This study focuses on the carbon exchange rate. We measure the emitted and absorbed CO2 fluxes by applying the closed chamber method. The biomass measurement and leaf area index (LAI) calculations at different plant growth stages are used to evaluate carbon exchange in different agroecosystems. The differences in photosynthetically assimilated CO2 rates were significantly impacted by the leaf area index (p = 0.04) during the plant vegetation period. The significantly (p = 0.02–0.05) strong correlation (r = 0.6–0.7) exists between soil respiration and LAI. Soil respiration composed only 21% of the agroecosystem carbon exchange. Plant respiration ranged between 0.034 and 3.613 µmol m−2 s−1 during the vegetation period composed of a negligible ratio (mean 16%) of carbon exchange. Generally, respiration emissions were obviously recovered by the gross primary production (GPP) of crops. Therefore, the ecosystems were acting as an atmospheric CO2 sink. Barley accumulated the lowest mean GPP 12.77 µmol m−2 s−1. The highest mean GPP was determined for ley (14.28 µmol m−2 s−1) and maize (15.68 µmol m−2 s−1) due to the biggest LAI and particular bio-characteristics. Due to the highest NEP, the ley (12.66 µmol m−2 s−1) and maize (12.76 µmol m−2 s−1) agroecosystems sank the highest C from the atmosphere and, thus, they might be considered the most sustainable items between crops. Consequently, the appropriate choice of crops and their area in crop rotations may reduce CO2 emissions and their impact on the environment and climate change.

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